We build the internal applications asset-protection teams run on: perpetual inventory reconciliation, risk-scored exceptions, and investigation case work, with AI woven through every screen and every number grounded in your own data.
Most asset-protection tools are general retail analytics with a loss-prevention label. They score transactions on someone else’s cloud, against a shared consortium of other retailers’ data, and hand back a black-box number you can’t take to an investigation. That is a hard place to defend a decision from.
We build the other thing: a purpose-built reconciliation and exception platform that runs on your own inventory movement data, understands the way your business actually loses product, and can explain every flag down to the ledger row that produced it. It is the difference between a dashboard that says shrink is up and a case file an investigator can act on.
The loop below is the one asset protection actually runs: reconcile expected against actual, rank the variances that matter, and turn the ones that hold up into cases. Here is how each step shows up in the app.
The platform keeps a perpetual ledger of every inventory movement, computes what each store’s balance should be, and surfaces the gap. A recon-health score, dollarized exposure, and a shrink trend tell a district lead where to look before the morning huddle, not a month later in a report.
Every anomaly is scored against a peer baseline, not a fixed threshold, so a store gets compared to stores like it. Each row carries a dollar figure and a plain-language reason, and the queue leads with what is worth an investigator’s time. Damage spikes that mask theft, void clusters on one associate, transfers that never arrived: named, not buried.
A confirmed exception becomes a case with the evidence already attached: the movement ledger, the employee attribution, the peer comparison, the timeline of what fired and when. Assignment, status, and every action are captured on an immutable audit trail, so the resolution holds up to review. Coach where it is a process error, escalate where it is not.
A newsroom agent writes the headlines from your live data the moment it loads. Insight cards explain each exception in plain English. A daily brief lands in the inbox at 6am, next-best-action is on every case, and an ask-your-data copilot answers in context, scoped to your stores. Every number comes from a query, not the model, and every claim links back to the rows behind it.
Runs on your own inventory data, not a third-party consortium of other retailers. Sensitive movement data never leaves to be scored elsewhere.
Every figure comes from a deterministic query over your data model. The AI phrases and prioritizes; it never invents a number. Each claim is cited to the row behind it.
Nothing about an associate auto-resolves. AI recommends, a person decides, and every attribution is gated and audit-logged.
First-class ingestion monitoring answers the question every AP data team actually loses sleep over: did last night’s load land, and can I trust today’s numbers?
A perpetual-inventory reconciliation and exception platform for a national pharmacy retailer’s asset-protection organization: movement-pattern anomaly detection, peer-baselined variance scoring, and an investigation case workflow, pharmacy-aware and built on the client’s own data. Engagement details are held in confidence.
Talk to our asset-protection team →Reconciliation, exception scoring, and investigation case work, on your own data. We will show you what it looks like on yours.
Talk to our asset-protection team →